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        <title>A survey of embedding models of entities and relationships for knowledge graph completion</title>
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            <title>Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)</title>
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            <namePart type="family">Ustalov</namePart>
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            <namePart type="given">Swapna</namePart>
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    <abstract>Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.</abstract>
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%0 Conference Proceedings
%T A survey of embedding models of entities and relationships for knowledge graph completion
%A Nguyen, Dat Quoc
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Hulpuș, Ioana
%Y Jansen, Peter
%Y Jana, Abhik
%S Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F nguyen-2020-survey
%X Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.
%R 10.18653/v1/2020.textgraphs-1.1
%U https://aclanthology.org/2020.textgraphs-1.1/
%U https://doi.org/10.18653/v1/2020.textgraphs-1.1
%P 1-14

Markdown (Informal)

[A survey of embedding models of entities and relationships for knowledge graph completion](https://aclanthology.org/2020.textgraphs-1.1/) (Nguyen, TextGraphs 2020)

• A survey of embedding models of entities and relationships for knowledge graph completion (Nguyen, TextGraphs 2020)

ACL

• Dat Quoc Nguyen. 2020. A survey of embedding models of entities and relationships for knowledge graph completion. In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs), pages 1–14, Barcelona, Spain (Online). Association for Computational Linguistics.